

from __future__ import annotations

import copy
import inspect
import json
import math
import os
import tempfile
import unittest
from importlib import import_module

import numpy as np

from transformers import ViTMAEConfig
from transformers.file_utils import cached_property, is_tf_available, is_vision_available
from transformers.testing_utils import require_tf, require_vision, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
    import tensorflow as tf

    from transformers import TFViTMAEForPreTraining, TFViTMAEModel


if is_vision_available():
    from PIL import Image

    from transformers import ViTImageProcessor

class     __snake_case    :
       """simple docstring"""







       def __init__(		self      , _UpperCamelCase      , _UpperCamelCase=13      , _UpperCamelCase=30      , _UpperCamelCase=2      , _UpperCamelCase=3      , _UpperCamelCase=True      , _UpperCamelCase=True      , _UpperCamelCase=32      , _UpperCamelCase=2      , _UpperCamelCase=4      , _UpperCamelCase=37      , _UpperCamelCase="gelu"      , _UpperCamelCase=0.1      , _UpperCamelCase=0.1      , _UpperCamelCase=10      , _UpperCamelCase=0.02      , _UpperCamelCase=3      , _UpperCamelCase=0.6      , _UpperCamelCase=None      , )      ->							Union[str, Any]:






              """simple docstring"""


              __snake_case       	=							parent
              __snake_case       	=							batch_size
              __snake_case       	=							image_size
              __snake_case       	=							patch_size
              __snake_case       	=							num_channels
              __snake_case       	=							is_training
              __snake_case       	=							use_labels
              __snake_case       	=							hidden_size
              __snake_case       	=							num_hidden_layers
              __snake_case       	=							num_attention_heads
              __snake_case       	=							intermediate_size
              __snake_case       	=							hidden_act
              __snake_case       	=							hidden_dropout_prob
              __snake_case       	=							attention_probs_dropout_prob
              __snake_case       	=							type_sequence_label_size
              __snake_case       	=							initializer_range
              __snake_case       	=							mask_ratio
              __snake_case       	=							scope

              # in ViTMAE, the expected sequence length = (num_patches + 1) * (1 - config.mask_ratio), rounded above
              # (we add 1 for the [CLS] token)
              __snake_case       	=							(image_size // patch_size) ** 2
              __snake_case       	=							int(math.ceil((1 - mask_ratio) * (num_patches + 1)   )   )


       def 			a		(		self   )      ->							Optional[int]:






              """simple docstring"""


              __snake_case       	=							floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]   )

              __snake_case       	=							None
              if self.use_labels:
                     __snake_case       	=							ids_tensor([self.batch_size]      , self.type_sequence_label_size   )

              __snake_case       	=							self.get_config()

              return config, pixel_values, labels


       def 			a		(		self   )      ->							List[str]:






              """simple docstring"""


              return ViTMAEConfig(
                  image_size=self.image_size      , patch_size=self.patch_size      , num_channels=self.num_channels      , hidden_size=self.hidden_size      , num_hidden_layers=self.num_hidden_layers      , num_attention_heads=self.num_attention_heads      , intermediate_size=self.intermediate_size      , decoder_hidden_size=self.hidden_size      , decoder_num_hidden_layers=self.num_hidden_layers      , decoder_num_attention_heads=self.num_attention_heads      , decoder_intermediate_size=self.intermediate_size      , hidden_act=self.hidden_act      , hidden_dropout_prob=self.hidden_dropout_prob      , attention_probs_dropout_prob=self.attention_probs_dropout_prob      , is_decoder=lowerCamelCase_      , initializer_range=self.initializer_range      , mask_ratio=self.mask_ratio      , )


       def 			a		(		self      , _UpperCamelCase      , _UpperCamelCase      , _UpperCamelCase   )      ->							Union[str, Any]:






              """simple docstring"""


              __snake_case       	=							TFViTMAEModel(config=lowerCamelCase_   )
              __snake_case       	=							model(lowerCamelCase_      , training=lowerCamelCase_   )
              self.parent.assertEqual(result.last_hidden_state.shape      , (self.batch_size, self.seq_length, self.hidden_size)   )


       def 			a		(		self      , _UpperCamelCase      , _UpperCamelCase      , _UpperCamelCase   )      ->							Optional[int]:






              """simple docstring"""


              __snake_case       	=							TFViTMAEForPreTraining(lowerCamelCase_   )
              __snake_case       	=							model(lowerCamelCase_      , training=lowerCamelCase_   )
              # expected sequence length = num_patches
              __snake_case       	=							(self.image_size // self.patch_size) ** 2
              __snake_case       	=							self.patch_size**2 * self.num_channels
              self.parent.assertEqual(result.logits.shape      , (self.batch_size, num_patches, expected_num_channels)   )

              # test greyscale images
              __snake_case       	=							1
              __snake_case       	=							TFViTMAEForPreTraining(lowerCamelCase_   )

              __snake_case       	=							floats_tensor([self.batch_size, 1, self.image_size, self.image_size]   )
              __snake_case       	=							model(lowerCamelCase_      , training=lowerCamelCase_   )
              __snake_case       	=							self.patch_size**2
              self.parent.assertEqual(result.logits.shape      , (self.batch_size, num_patches, expected_num_channels)   )






       def 			a		(		self   )      ->							Optional[Any]:






              """simple docstring"""


              __snake_case       	=							self.prepare_config_and_inputs()
              ((__snake_case)	,    (__snake_case)	,    (__snake_case))       	=							config_and_inputs
              __snake_case       	=							{"""pixel_values""": pixel_values}
              return config, inputs_dict






@require_tf
class     __snake_case    (				__lowerCAmelCase			,	__lowerCAmelCase			,	unittest.TestCase  ):
       """simple docstring"""







       __SCREAMING_SNAKE_CASE				    =							(TFViTMAEModel, TFViTMAEForPreTraining) if is_tf_available() else ()
       __SCREAMING_SNAKE_CASE				    =							{"feature-extraction": TFViTMAEModel} if is_tf_available() else {}

       __SCREAMING_SNAKE_CASE				    =							False
       __SCREAMING_SNAKE_CASE				    =							False
       __SCREAMING_SNAKE_CASE				    =							False
       __SCREAMING_SNAKE_CASE				    =							False


       def 			a		(		self   )      ->							Tuple:






              """simple docstring"""


              __snake_case       	=							TFViTMAEModelTester(self   )
              __snake_case       	=							ConfigTester(self      , config_class=lowerCamelCase_      , has_text_modality=lowerCamelCase_      , hidden_size=37   )


       def 			a		(		self   )      ->							Tuple:






              """simple docstring"""


              self.config_tester.run_common_tests()


       @unittest.skip(reason="""ViTMAE does not use inputs_embeds"""   )
       def 			a		(		self   )      ->							Optional[Any]:






              """simple docstring"""


              pass


       def 			a		(		self   )      ->							Dict:






              """simple docstring"""


              __snake_case	,    __snake_case       	=							self.model_tester.prepare_config_and_inputs_for_common()

              for model_class in self.all_model_classes:
                     __snake_case       	=							model_class(lowerCamelCase_   )
                     self.assertIsInstance(model.get_input_embeddings()      , (tf.keras.layers.Layer)   )
                     __snake_case       	=							model.get_output_embeddings()
                     self.assertTrue(x is None or isinstance(lowerCamelCase_      , tf.keras.layers.Layer   )   )


       def 			a		(		self   )      ->							int:






              """simple docstring"""


              __snake_case	,    __snake_case       	=							self.model_tester.prepare_config_and_inputs_for_common()

              for model_class in self.all_model_classes:
                     __snake_case       	=							model_class(lowerCamelCase_   )
                     __snake_case       	=							inspect.signature(model.call   )
                     # signature.parameters is an OrderedDict => so arg_names order is deterministic
                     __snake_case       	=							[*signature.parameters.keys()]

                     __snake_case       	=							["""pixel_values"""]
                     self.assertListEqual(arg_names[:1]      , lowerCamelCase_   )


       def 			a		(		self   )      ->							int:






              """simple docstring"""


              __snake_case       	=							self.model_tester.prepare_config_and_inputs()
              self.model_tester.create_and_check_model(*lowerCamelCase_   )


       def 			a		(		self   )      ->							Tuple:






              """simple docstring"""


              __snake_case       	=							self.model_tester.prepare_config_and_inputs()
              self.model_tester.create_and_check_for_pretraining(*lowerCamelCase_   )


       def 			a		(		self   )      ->							Tuple:






              """simple docstring"""


              np.random.seed(2   )

              __snake_case	,    __snake_case       	=							self.model_tester.prepare_config_and_inputs_for_common()

              __snake_case       	=							int((config.image_size // config.patch_size) ** 2   )
              __snake_case       	=							np.random.uniform(size=(self.model_tester.batch_size, num_patches)   )

              for model_class in self.all_model_classes:
                     __snake_case       	=							model_class(lowerCamelCase_   )
                     __snake_case       	=							self._prepare_for_class(lowerCamelCase_      , lowerCamelCase_   )

                     __snake_case       	=							model(lowerCamelCase_      , noise=lowerCamelCase_   )

                     __snake_case       	=							copy.deepcopy(self._prepare_for_class(lowerCamelCase_      , lowerCamelCase_   )   )
                     __snake_case       	=							model(**lowerCamelCase_      , noise=lowerCamelCase_   )
                     __snake_case       	=							outputs_dict[0].numpy()
                     __snake_case       	=							outputs_keywords[0].numpy()

                     self.assertLess(np.sum(np.abs(output_dict - output_keywords   )   )      , 1E-6   )


       def 			a		(		self   )      ->							Union[str, Any]:






              """simple docstring"""


              np.random.seed(2   )

              __snake_case	,    __snake_case       	=							self.model_tester.prepare_config_and_inputs_for_common()

              __snake_case       	=							int((config.image_size // config.patch_size) ** 2   )
              __snake_case       	=							np.random.uniform(size=(self.model_tester.batch_size, num_patches)   )

              def prepare_numpy_arrays(_UpperCamelCase   ):
                     __snake_case       	=							{}
                     for k, v in inputs_dict.items():
                            if tf.is_tensor(lowerCamelCase_   ):
                                   __snake_case       	=							v.numpy()
                            else:
                                   __snake_case       	=							np.array(lowerCamelCase_   )

                     return inputs_np_dict

              for model_class in self.all_model_classes:
                     __snake_case       	=							model_class(lowerCamelCase_   )

                     __snake_case       	=							self._prepare_for_class(lowerCamelCase_      , lowerCamelCase_   )
                     __snake_case       	=							prepare_numpy_arrays(lowerCamelCase_   )

                     __snake_case       	=							model(lowerCamelCase_      , noise=lowerCamelCase_   )
                     __snake_case       	=							model(**lowerCamelCase_      , noise=lowerCamelCase_   )
                     self.assert_outputs_same(lowerCamelCase_      , lowerCamelCase_   )


       def 			a		(		self      , _UpperCamelCase      , _UpperCamelCase      , _UpperCamelCase   )      ->							Optional[int]:






              """simple docstring"""


              np.random.seed(2   )

              __snake_case       	=							int((tf_model.config.image_size // tf_model.config.patch_size) ** 2   )
              __snake_case       	=							np.random.uniform(size=(self.model_tester.batch_size, num_patches)   )
              __snake_case       	=							tf.constant(lowerCamelCase_   )

              # Add `noise` argument.
              # PT inputs will be prepared in `super().check_pt_tf_models()` with this added `noise` argument
              __snake_case       	=							tf_noise

              super().check_pt_tf_models(lowerCamelCase_      , lowerCamelCase_      , lowerCamelCase_   )


       def 			a		(		self   )      ->							str:






              """simple docstring"""


              np.random.seed(2   )

              __snake_case	,    __snake_case       	=							self.model_tester.prepare_config_and_inputs_for_common()

              __snake_case       	=							{
                  module_member
                  for model_class in self.all_model_classes
                  for module in (import_module(model_class.__module__   ),)
                  for module_member_name in dir(lowerCamelCase_   )
                  if module_member_name.endswith("""MainLayer"""   )
                  # This condition is required, since `modeling_tf_clip.py` has 3 classes whose names end with `MainLayer`.
                  and module_member_name[: -len("""MainLayer"""   )] == model_class.__name__[: -len("""Model"""   )]
                  for module_member in (getattr(lowerCamelCase_      , lowerCamelCase_   ),)
                  if isinstance(lowerCamelCase_      , lowerCamelCase_   )
                  and tf.keras.layers.Layer in module_member.__bases__
                  and getattr(lowerCamelCase_      , """_keras_serializable"""      , lowerCamelCase_   )
              }

              __snake_case       	=							int((config.image_size // config.patch_size) ** 2   )
              __snake_case       	=							np.random.uniform(size=(self.model_tester.batch_size, num_patches)   )
              __snake_case       	=							tf.convert_to_tensor(lowerCamelCase_   )
              inputs_dict.update({"""noise""": noise}   )

              for main_layer_class in tf_main_layer_classes:
                     __snake_case       	=							main_layer_class(lowerCamelCase_   )

                     __snake_case       	=							{
                         name: tf.keras.Input(tensor.shape[1:]      , dtype=tensor.dtype   ) for name, tensor in inputs_dict.items()
                     }

                     __snake_case       	=							tf.keras.Model(lowerCamelCase_      , outputs=main_layer(lowerCamelCase_   )   )
                     __snake_case       	=							model(lowerCamelCase_   )

                     with tempfile.TemporaryDirectory() as tmpdirname:
                            __snake_case       	=							os.path.join(lowerCamelCase_      , """keras_model.h5"""   )
                            model.save(lowerCamelCase_   )
                            __snake_case       	=							tf.keras.models.load_model(
                                lowerCamelCase_      , custom_objects={main_layer_class.__name__: main_layer_class}   )
                            assert isinstance(lowerCamelCase_      , tf.keras.Model   )
                            __snake_case       	=							model(lowerCamelCase_   )
                            self.assert_outputs_same(lowerCamelCase_      , lowerCamelCase_   )


       @slow
       def 			a		(		self   )      ->							int:






              """simple docstring"""


              np.random.seed(2   )

              __snake_case	,    __snake_case       	=							self.model_tester.prepare_config_and_inputs_for_common()

              __snake_case       	=							int((config.image_size // config.patch_size) ** 2   )
              __snake_case       	=							np.random.uniform(size=(self.model_tester.batch_size, num_patches)   )

              for model_class in self.all_model_classes:
                     __snake_case       	=							model_class(lowerCamelCase_   )
                     __snake_case       	=							self._prepare_for_class(lowerCamelCase_      , lowerCamelCase_   )
                     __snake_case       	=							model(lowerCamelCase_      , noise=lowerCamelCase_   )

                     if model_class.__name__ == "TFViTMAEModel":
                            __snake_case       	=							outputs.last_hidden_state.numpy()
                            __snake_case       	=							0
                     else:
                            __snake_case       	=							outputs.logits.numpy()
                            __snake_case       	=							0

                     with tempfile.TemporaryDirectory() as tmpdirname:
                            model.save_pretrained(lowerCamelCase_      , saved_model=lowerCamelCase_   )
                            __snake_case       	=							model_class.from_pretrained(lowerCamelCase_   )
                            __snake_case       	=							model(lowerCamelCase_      , noise=lowerCamelCase_   )

                            if model_class.__name__ == "TFViTMAEModel":
                                   __snake_case       	=							after_outputs["""last_hidden_state"""].numpy()
                                   __snake_case       	=							0
                            else:
                                   __snake_case       	=							after_outputs["""logits"""].numpy()
                                   __snake_case       	=							0

                            __snake_case       	=							np.amax(np.abs(out_a - out_a   )   )
                            self.assertLessEqual(lowerCamelCase_      , 1E-5   )


       def 			a		(		self   )      ->							List[str]:






              """simple docstring"""


              np.random.seed(2   )

              __snake_case	,    __snake_case       	=							self.model_tester.prepare_config_and_inputs_for_common()

              __snake_case       	=							int((config.image_size // config.patch_size) ** 2   )
              __snake_case       	=							np.random.uniform(size=(self.model_tester.batch_size, num_patches)   )

              for model_class in self.all_model_classes:
                     __snake_case       	=							model_class(lowerCamelCase_   )
                     __snake_case       	=							self._prepare_for_class(lowerCamelCase_      , lowerCamelCase_   )

                     __snake_case       	=							model(lowerCamelCase_      , noise=lowerCamelCase_   )
                     __snake_case       	=							model.get_config()
                     # make sure that returned config is jsonifiable, which is required by keras
                     json.dumps(lowerCamelCase_   )
                     __snake_case       	=							model_class.from_config(model.get_config()   )
                     # make sure it also accepts a normal config
                     __snake_case       	=							model_class.from_config(model.config   )
                     __snake_case       	=							new_model(lowerCamelCase_   )  # Build model
                     new_model.set_weights(model.get_weights()   )
                     __snake_case       	=							new_model(lowerCamelCase_      , noise=lowerCamelCase_   )

                     self.assert_outputs_same(lowerCamelCase_      , lowerCamelCase_   )


       @unittest.skip(
           reason="""ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load
    to get deterministic results."""   )
       def 			a		(		self   )      ->							Optional[int]:






              """simple docstring"""


              pass


       @unittest.skip(reason="""ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load"""   )
       def 			a		(		self   )      ->							Optional[Any]:






              """simple docstring"""


              pass






       @slow
       def 			a		(		self   )      ->							Any:






              """simple docstring"""


              __snake_case       	=							TFViTMAEModel.from_pretrained("""google/vit-base-patch16-224"""   )
              self.assertIsNotNone(lowerCamelCase_   )







def 						lowerCamelCase__      ( ):


       """simple docstring"""




       __snake_case       	=							Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""		)
       return image




@require_tf
@require_vision
class     __snake_case    (				unittest.TestCase  ):
       """simple docstring"""







       @cached_property
       def 			a		(		self   )      ->							Union[str, Any]:






              """simple docstring"""


              return ViTImageProcessor.from_pretrained("""facebook/vit-mae-base"""   ) if is_vision_available() else None






       @slow
       def 			a		(		self   )      ->							Dict:






              """simple docstring"""


              np.random.seed(2   )

              __snake_case       	=							TFViTMAEForPreTraining.from_pretrained("""facebook/vit-mae-base"""   )

              __snake_case       	=							self.default_image_processor
              __snake_case       	=							prepare_img()
              __snake_case       	=							image_processor(images=lowerCamelCase_      , return_tensors="""tf"""   )

              # prepare a noise vector that will be also used for testing the TF model
              # (this way we can ensure that the PT and TF models operate on the same inputs)
              __snake_case       	=							ViTMAEConfig()
              __snake_case       	=							int((vit_mae_config.image_size // vit_mae_config.patch_size) ** 2   )
              __snake_case       	=							np.random.uniform(size=(1, num_patches)   )

              # forward pass
              __snake_case       	=							model(**lowerCamelCase_      , noise=lowerCamelCase_   )

              # verify the logits
              __snake_case       	=							tf.convert_to_tensor([1, 1_96, 7_68]   )
              self.assertEqual(outputs.logits.shape      , lowerCamelCase_   )

              __snake_case       	=							tf.convert_to_tensor(
                  [[-0.0548, -1.7023, -0.9325], [0.3721, -0.5670, -0.2233], [0.8235, -1.3878, -0.3524]]   )

              tf.debugging.assert_near(outputs.logits[0, :3, :3]      , lowerCamelCase_      , atol=1E-4   )


# This script creates a super tiny model that is useful inside tests, when we just want to test that
# the machinery works, without needing to the check the quality of the outcomes.
#
# This version creates a tiny vocab first, and then a tiny model - so the outcome is truly tiny -
# all files ~60KB. As compared to taking a full-size model, reducing to the minimum its layers and
# emb dimensions, but keeping the full vocab + merges files, leading to ~3MB in total for all files.
# The latter is done by `fsmt-make-super-tiny-model.py`.
#
# It will be used then as "stas/tiny-wmt19-en-ru"

from pathlib import Path
import json
import tempfile

from transformers import FSMTTokenizer, FSMTConfig, FSMTForConditionalGeneration
from transformers.models.fsmt.tokenization_fsmt import VOCAB_FILES_NAMES

_SCREAMING_SNAKE_CASE 		=      """tiny-wmt19-en-ru"""

# Build

# borrowed from a test
_SCREAMING_SNAKE_CASE 		=      [
    """l""",
    """o""",
    """w""",
    """e""",
    """r""",
    """s""",
    """t""",
    """i""",
    """d""",
    """n""",
    """w</w>""",
    """r</w>""",
    """t</w>""",
    """lo""",
    """low""",
    """er</w>""",
    """low</w>""",
    """lowest</w>""",
    """newer</w>""",
    """wider</w>""",
    """<unk>""",
]
_SCREAMING_SNAKE_CASE 		=      dict(zip(vocab, range(len(vocab))))
_SCREAMING_SNAKE_CASE 		=      ["""l o 123""", """lo w 1456""", """e r</w> 1789""", """"""]

with tempfile.TemporaryDirectory() as tmpdirname:
     _SCREAMING_SNAKE_CASE 		=      Path(tmpdirname)
     _SCREAMING_SNAKE_CASE 		=      build_dir / VOCAB_FILES_NAMES["""src_vocab_file"""]
     _SCREAMING_SNAKE_CASE 		=      build_dir / VOCAB_FILES_NAMES["""tgt_vocab_file"""]
     _SCREAMING_SNAKE_CASE 		=      build_dir / VOCAB_FILES_NAMES["""merges_file"""]
     with open(src_vocab_file, """w""") as fp:
          fp.write(json.dumps(vocab_tokens))
     with open(tgt_vocab_file, """w""") as fp:
          fp.write(json.dumps(vocab_tokens))
     with open(merges_file, """w""") as fp:
          fp.write("""\n""".join(merges))

     _SCREAMING_SNAKE_CASE 		=      FSMTTokenizer(
         langs=["""en""", """ru"""],
         src_vocab_size=len(vocab),
         tgt_vocab_size=len(vocab),
         src_vocab_file=src_vocab_file,
         tgt_vocab_file=tgt_vocab_file,
         merges_file=merges_file,
     )

_SCREAMING_SNAKE_CASE 		=      FSMTConfig(
    langs=["""ru""", """en"""],
    src_vocab_size=1_0_0_0,
    tgt_vocab_size=1_0_0_0,
    d_model=4,
    encoder_layers=1,
    decoder_layers=1,
    encoder_ffn_dim=4,
    decoder_ffn_dim=4,
    encoder_attention_heads=1,
    decoder_attention_heads=1,
)

_SCREAMING_SNAKE_CASE 		=      FSMTForConditionalGeneration(config)
print(F'''num of params {tiny_model.num_parameters()}''')

# Test
_SCREAMING_SNAKE_CASE 		=      tokenizer(["""Making tiny model"""], return_tensors="""pt""")
_SCREAMING_SNAKE_CASE 		=      tiny_model(**batch)

print("""test output:""", len(outputs.logits[0]))

# Save
tiny_model.half()  # makes it smaller
tiny_model.save_pretrained(mname_tiny)
tokenizer.save_pretrained(mname_tiny)

print(F'''Generated {mname_tiny}''')

# Upload
# transformers-cli upload tiny-wmt19-en-ru
